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Physics Informed Machine Learning Jobs in Colorado Springs, CO

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Physics Informed Machine Learning information

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How much do physics informed machine learning jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for physics informed machine learning in Colorado Springs, CO is $19.77, according to ZipRecruiter salary data. Most workers in this role earn between $12.31 and $25.10 per hour, depending on experience, location, and employer.

What is a physics informed machine learning?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

What are the typical challenges faced by professionals working in physics informed machine learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What are the key skills and qualifications needed to thrive in the physics informed machine learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are popular job titles related to Physics Informed Machine Learning jobs in Colorado Springs, CO?

For Physics Informed Machine Learning jobs in Colorado Springs, CO, the most frequently searched job titles are:

What cities near Colorado Springs, CO are hiring for Physics Informed Machine Learning jobs?

Cities near Colorado Springs, CO with the most Physics Informed Machine Learning job openings:

Infographic showing various Physics Informed Machine Learning job openings in Colorado Springs, CO as of August 2026, with employment types broken down into 6% Internship, 42% Full Time, 45% Part Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $41,125 per year, or $19.8 per hour.

Modeling and Simulation Engineer

Booz Allen Hamilton

Colorado Springs, CO • On-site

$86K - $198K/yr

Other

Medical, Life, Retirement, PTO

Posted 15 days ago


Booz Allen Hamilton rating

8.9

Company rating: 8.9 out of 10

Based on 49 frontline employees who took The Breakroom Quiz

9th of 72 rated business consultants


Job description

Job Number: R0245998
Modeling and Simulation Engineer
The Opportunity:
Design and tailor software solutions using high-level programming language such as Java, C++, and Python to inform modeling, wargaming, operational concepts and force design recommendations for the United States Space Force (USSF). Build and maintain core simulation tools that underpin the entire command and control (C2) analysis suite. Generate clean, efficient code to integrate new analytic models and behaviors, and improve existing codebases, ensuring optimal software performance and reliability. Perform thorough analysis of both structured data such as databases, spreadsheets, and quantitative datasets and unstructured data including text documents, sensor outputs, and system logs to inform essential software development choices and ensure the accuracy of models utilized in USSF simulation activities. Leverage advanced data mining and machine learning techniques to extract actionable insights, identifying patterns and anomalies that influence modeling, simulation, and operational concepts. Collaborate with cross-functional teams, research analysts, and operational C2 subject matter experts to translate complex data findings into scalable, reliable software features. Prototype innovative software features and optimize simulation tools to ensure software solutions are robust, adaptable, and aligned with the evolving needs of the USSF. Implement and ship modular software updates, optimize simulation runtime performance, and ensure smooth data exchange between C2 software interface and backend simulation logic. Leverage experience with version control systems, documentation practices, and Git workflows to maintain collaborative project environments.
You Have:
  • 3+ years of experience developing software in Java, C++, C, or Python
  • 3+ years of experience analyzing structured and unstructured data sources
  • Experience generating clean, efficient source code for new applications and enhancing existing codebases
  • Experience building high fidelity models within environments such as AFSIM, NGTS, or ITASE
  • Knowledge of version control systems and Git workflows to manage collaborative projects
  • Active TS/SCI clearance; willingness to take a polygraph exam
  • Bachelor's degree

Nice If You Have:
  • Experience with tools such as AnyLogic or ExtendSim
  • Experience with visualization packages, including Plotly, Seaborn, or ggplot2
  • Knowledge of National Security Space systems and architectures
  • Master's degree in Physical Science, Engineering, Physics, Mathematics, Operational Research, or Computer Science

Clearance:
Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information; TS/SCI clearance is required.
Compensation
At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen's benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page.
Salary at Booz Allen is determined by various factors, including but not limited to location, the individual's particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $86,800.00 to $198,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen's total compensation package for employees. This posting will close within 90 days from the Posting Date.
Identity Statement
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Candidate AI Usage Policy
AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided.
Work Model
Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings.
  • Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility.
  • Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility.
  • Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role.

Commitment to Non-Discrimination
All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.

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About Booz Allen Hamilton

Sourced by ZipRecruiter

Booz Allen Hamilton is a leading provider of management and technology consulting services to the US government in defense, intelligence, and civil markets. Headquartered in McLean, Virginia, the firm also serves major corporations, institutions, and not-for-profit organizations. Founded in 1914 by Edwin G. Booz, the company has a long-standing tradition of helping clients achieve success by delivering a wide range of consulting services that include strategic planning, human capital and learning, communication, systems development, and others. The company's mission is to empower people to change the world, and it has a reputation for maintaining the highest standards of integrity and-excellence.

Industry

It services

Company size

10,000+ Employees

Headquarters location

McLean, VA, US

Year founded

1914